{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/andre/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "/Users/andre/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "/Users/andre/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "/Users/andre/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "/Users/andre/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "/Users/andre/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "/Users/andre/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "/Users/andre/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "/Users/andre/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "/Users/andre/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "/Users/andre/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=3, random_state=42)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "# Загрузка датасета и отброс целевой переменной\n",
    "iris = load_iris()\n",
    "data = iris.data[:, :2]  # оставляем только sepal_length и sepal_width\n",
    "\n",
    "# Построение модели KMeans и выбор оптимального числа кластеров\n",
    "inertia = []\n",
    "for n_clusters in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
    "    kmeans.fit(data)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "\n",
    "x_for_viz = []\n",
    "y_for_viz = []\n",
    "\n",
    "# Обучение модели с оптимальным числом кластеров и визуализация кластеров\n",
    "optimal_n_clusters = 3\n",
    "kmeans = KMeans(n_clusters=optimal_n_clusters, random_state=42)\n",
    "kmeans.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Визуализация \"локтя\" для выбора оптимального числа кластеров\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(range(1, 11), inertia, marker='o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# визуализация полученной кластеризации\n",
    "for i in data:\n",
    "    x_for_viz.append(i[0])\n",
    "    y_for_viz.append(i[1])\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(x_for_viz, y_for_viz, c=kmeans.labels_, cmap='viridis', edgecolor='k')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], c='red', marker='X', s=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
